Endocrine Oncology (Nov 2024)

Multifaceted modeling of small intestinal neuroendocrine tumors

  • Stephen Gabriel Andrews,
  • Steven D Forsythe,
  • James P Madigan,
  • Samira Mercedes Sadowski

DOI
https://doi.org/10.1530/EO-24-0038
Journal volume & issue
Vol. 4, no. 1
pp. 1 – 13

Abstract

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Small intestinal neuroendocrine tumors, siNETs, are a group of rare cancers that arise from neuroendocrine cells in the lining of the jejunum and ileum, which are either classified as tumors, siNETs, or small intestinal neuroendocrine carcinomas, siNECs. Current treatment strategies for low-grade tumors include surgical resection, peptide radionucleotide receptor therapy, and somatostatin analogues, while high-grade and recurrent tumors may receive cytotoxic chemotherapy. These limited treatment options are linked to the lack of representative models that can both reflect the biology of the tumor and are amenable to mid-to-high throughput experimentation. Cell line generation is challenging considering the indolent nature of primary lesions, although some attempts have been successful using a variety of methods and include the primary P-STS line and those derived from metastatic lesions, including GOT1, CNDT2.5, and HC45. Patient-derived modeling, including organoids and xenografting, have allowed for multicellular and 3D representations of the original tumor. These specific models allow for multicellular populations derived from the tumor, providing better tumor representation for use in drug screening and in vitro assays. Currently, there are limited, although increasing, published models of siNETs implanted as xenografts in mice and zebrafish. As these cellular and animal models provide insights into siNET biology, theragnostic modeling has provided key information on the clinical progression and treatment of this disease. Significant strides toward more representative models have been made throughout the last decade. In this review, details of these attempts as well as future directions and strategies for more robust models will be addressed.

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